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Rule-Based Automatic Question Generation Using Semantic Role Labeling
Onur KEKLIK Tugkan TUGLULAR Selma TEKIR
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/07/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Natural Language Processing
question generation, rule-based, semantic role labeling, METEOR,
Full Text: FreePDF(486.8KB)
This paper proposes a new rule-based approach to automatic question generation. The proposed approach focuses on analysis of both syntactic and semantic structure of a sentence. Although the primary objective of the designed system is question generation from sentences, automatic evaluation results shows that, it also achieves great performance on reading comprehension datasets, which focus on question generation from paragraphs. Especially, with respect to METEOR metric, the designed system significantly outperforms all other systems in automatic evaluation. As for human evaluation, the designed system exhibits similar performance by generating the most natural (human-like) questions.